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The Use of bioinformatics techniques to perform time-series trend matching and prediction
Dissertation (MEng)--University of Pretoria, 2012.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2014
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| _version_ | 1867613459026280448 |
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| access_status_str | Open Access |
| author2 | Sandrock, Carl |
| author_browse | Sandrock, Carl |
| author_facet | Sandrock, Carl |
| collection | Thesis |
| dc_rights_str_mv | © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2012. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/37061 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:28.597Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/37061 The Use of bioinformatics techniques to perform time-series trend matching and prediction Sandrock, Carl marktransell@gmail.com Transell, Mark Marriott Control Engineering Data Mining Signal Processing UCTD SAX Blast Motif-matching Dynamic time warping C14/4/163/gm Dissertation (MEng)--University of Pretoria, 2012. Process operators often have process faults and alarms due to recurring failures on process equipment. It is also the case that some processes do not have enough input information or process models to use conventional modelling or machine learning techniques for early fault detection. A proof of concept for online streaming prediction software based on matching process behaviour to historical motifs has been developed, making use of the Basic Local Alignment Search Tool (BLAST) used in the Bioinformatics field. Execution times of as low as 1 second have been recorded, demonstrating that online matching is feasible. Three techniques have been tested and compared in terms of their computational effciency, robustness and selectivity, with results shown in Table 1: • Symbolic Aggregate Approximation combined with PSI-BLAST • Naive Triangular Representation with PSI-BLAST • Dynamic Time Warping Table 1: Properties of different motif-matching methods Property SAX-PSIBLAST TER-PSIBLAST DTW Noise tolerance (Selectivity) Acceptable Inconclusive Good Vertical Shift tolerance None Perfect Poor Matching speed Acceptable Acceptable Fast Match speed scaling O < O(mn) O < O(mn) O(mn) Dimensionality Reduction Tolerance Good Inconclusive Acceptable It is recommended that a method using a weighted confidence measure for each technique be investigated for the purpose of online process event handling and operator alerts. Keywords: SAX, BLAST, motif-matching, Dynamic Time Warping Chemical Engineering unrestricted 2014-03-04T11:43:59Z 2014-03-04T11:43:59Z 2014 2012 Dissertation Transell, MM 2014, The Use of bioinformatics techniques to perform time-series trend matching and prediction, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd<http://hdl.handle.net/2263/37061> http://hdl.handle.net/2263/37061 en © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Control Engineering Data Mining Signal Processing UCTD SAX Blast Motif-matching Dynamic time warping C14/4/163/gm The Use of bioinformatics techniques to perform time-series trend matching and prediction |
| title | The Use of bioinformatics techniques to perform time-series trend matching and prediction |
| title_full | The Use of bioinformatics techniques to perform time-series trend matching and prediction |
| title_fullStr | The Use of bioinformatics techniques to perform time-series trend matching and prediction |
| title_full_unstemmed | The Use of bioinformatics techniques to perform time-series trend matching and prediction |
| title_short | The Use of bioinformatics techniques to perform time-series trend matching and prediction |
| title_sort | use of bioinformatics techniques to perform time series trend matching and prediction |
| topic | Control Engineering Data Mining Signal Processing UCTD SAX Blast Motif-matching Dynamic time warping C14/4/163/gm |
| url | http://hdl.handle.net/2263/37061 |